- spectral analysis of baseflow and groundwater head fluctuations to derive effective regional aquifer parameters
- numerical groundwater modelling
- high performance computing
- goal oriented modelling and complexity assessment
- machine learning
- Klitzsch, N., Houben, T., Brunner, M., Kleinow, R., 2014: Geophysical prospection of siderite concretions in sediments – a feasibility study. Applied Geophysics and Geothermal Energy, E.ON Energy Research Center, RWTH Aachen University
- From dynamic groundwater head measurements to regional aquifer parameters., Houben, T., Kalbacher, T., Pujades, E., Dietrich, P., Attinger, S.. December 2019. AGU Fall Meeting 2019. San Francisco.
Presentation (9.8 MB)
- When does model complexity pay off? A case study for groundwater modelling., Houben, T., de Rooij, G., Attinger, S., Kalbacher, T., Dietrich, P., October 2018. TERENO International Conference 2018. Berlin.
- Regional aquifer parameters by spectral analysis of groundwater head fluctuations – a synthetic study. Houben, T., Kalbacher, T., Dietrich and S. Attinger. EGU General Assembly 2019. Vienna. EGU_19 (5.4 MB)
- Machine Learning Café. Chair: Timo Houben, Co-Chair: Lennart Schmidt, Swamini Khurana. You are new to machine learning and need an overview? Or you just want to talk about your challenges in your studies in an informal setting? Grab a coffee and visit us in Building 7.1, room 301 on Thursdays from 4:30 p.m. Be up to date and have a look at our GitLab-Repo. Looking forward to study with you!
- PhD-Team Data Science. Chair: Swamini Khurana, Co-Chair: Timo Houben. You are doing your PhD at UFZ and are interested in data science? Come and join us! Further info right here.